Gene Discovery for Enzymes Involved in Limonene Modification or Utilization by the Mountain Pine Beetle-Associated Pathogen Grosmannia clavigera
Bibliographic record
Abstract
To successfully colonize and eventually kill pine trees, Grosmannia clavigera (Gs cryptic species), the main fungal pathogen associated with the mountain pine beetle (Dendroctonus ponderosae), has developed multiple mechanisms to overcome host tree chemical defenses, of which terpenoids are a major component. In addition to a monoterpene efflux system mediated by a recently discovered ABC transporter, Gs has genes that are highly induced by monoterpenes and that encode enzymes that modify or utilize monoterpenes [especially (+)-limonene]. We showed that pine-inhabiting Ophiostomale fungi are tolerant to monoterpenes, but only a few, including Gs, are known to utilize monoterpenes as a carbon source. Gas chromatography-mass spectrometry (GC-MS) revealed that Gs can modify (+)-limonene through various oxygenation pathways, producing carvone, p-mentha-2,8-dienol, perillyl alcohol, and isopiperitenol. It can also degrade (+)-limonene through the C-1-oxygenated pathway, producing limonene-1,2-diol as the most abundant intermediate. Transcriptome sequencing (RNA-seq) data indicated that Gs may utilize limonene 1,2-diol through beta-oxidation and then valine and tricarboxylic acid (TCA) metabolic pathways. The data also suggested that at least two gene clusters, located in genome contigs 108 and 161, were highly induced by monoterpenes and may be involved in monoterpene degradation processes. Further, gene knockouts indicated that limonene degradation required two distinct Baeyer-Villiger monooxygenases (BVMOs), an epoxide hydrolase and an enoyl coenzyme A (enoyl-CoA) hydratase. Our work provides information on enzyme-mediated limonene utilization or modification and a more comprehensive understanding of the interaction between an economically important fungal pathogen and its host's defense chemicals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".